Investigating Violations of Parametric Test Assumptions in Tourism Research

Aim: This study evaluates the extent to which researchers adhere to parametric testing assumptions in quantitative studies published in SSCI-indexed tourism journals.Method: Using document analysis, 239 empirical articles published between 2020 and 2024 in 19 journals were systematically reviewed with a focus on the statistical methods employed and on whether basic parametric assumptions were acknowledged, tested, and reported.Results: The findings show a predominant use of SEM/CB-SEM and PLS-SEM. Data-type assumptions were mentioned in 94% of cases, whereas normality and sample-size assumptions were addressed far less frequently: only 43% of tests reported checking normality and 29% reported assessing sample size. Normality was not mentioned in 51% of tests and explicitly stated as “not needed” in 6%, while sample-size assumptions were not mentioned in 58% and claimed to be unnecessary in 13% of cases. When all parametric assumptions were considered together, only 24% of studies reported them comprehensively, whereas 57% did not mention them and 18% explicitly stated that such assumptions were not needed; all of these “not needed” cases employed PLS-SEM.Conclusion: These results highlight a need for greater methodological rigor in tourism research and underscore the importance of systematically considering parametric assumptions to strengthen the validity and reliability of quantitative findings in the field.